Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.pct_change() function calculates the percentage change between the current and a prior element. This function by default calculates the percentage change from the immediately previous row.
Note : This function is mostly useful in the time-series data.
Syntax: DataFrame.pct_change(periods=1, fill_method=’pad’, limit=None, freq=None, **kwargs)
Parameters :
periods : Periods to shift for forming percent change.
fill_method : How to handle NAs before computing percent changes.
limit : The number of consecutive NAs to fill before stopping
freq : Increment to use from time series API (e.g. ‘M’ or BDay()).
**kwargs : Additional keyword arguments are passed into DataFrame.shift or Series.shift.Returns : The same type as the calling object.
Example #1: Use pct_change() function to find the percentage change in the time-series data.
# importing pandas as pdimport pandas as pd # Creating the time-series indexind = pd.date_range('01/01/2000', periods = 6, freq ='W') # Creating the dataframe df = pd.DataFrame({"A":[14, 4, 5, 4, 1, 55], "B":[5, 2, 54, 3, 2, 32], "C":[20, 20, 7, 21, 8, 5], "D":[14, 3, 6, 2, 6, 4]}, index = ind) # Print the dataframedf |
Let’s use the dataframe.pct_change() function to find the percent change in the data.
# find the percentage change with the previous rowdf.pct_change() |
Output :
The first row contains NaN values, as there is no previous row from which we can calculate the change.
Example #2: Use pct_change() function to find the percentage change in the data which is also having NaN values.
# importing pandas as pdimport pandas as pd # Creating the time-series indexind = pd.date_range('01/01/2000', periods = 6, freq ='W') # Creating the dataframe df = pd.DataFrame({"A":[14, 4, 5, 4, 1, 55], "B":[5, 2, None, 3, 2, 32], "C":[20, 20, 7, 21, 8, None], "D":[14, None, 6, 2, 6, 4]}, index = ind) # apply the pct_change() method# we use the forward fill method to# fill the missing values in the dataframedf.pct_change(fill_method ='ffill') |
Output :
The first row contains NaN values, as there is no previous row from which we can calculate the change. All the NaN values in the dataframe has been filled using ffill method.
